Short Vs. Long Tail: Which Search Queries Perform Best?

The focus on the utility (or lack thereof) of long tail keywords in paid search campaigns seems to ebb and flow, but recently a series of articles about leveraging the long tail for pay-per click have been published. But in all the debate there’s an important distinction that most people fail to focus on.

Keywords and search queries are two very different entities. While keywords are the inventory an advertiser buys, search queries are the actual words people type into a search engine. The distinction is important, because search queries typically cover a significantly longer tail than keyword buys, and there is a lot of value in mining search engine query data.

To take a deeper dive and look at the way that head, mid-level, and long tail search queries perform in paid search accounts, we took a random sampling of WordStream client accounts and analyzed the aggregate cost, number of conversions, and cost-per conversion across the sample. The results were very interesting, and to some extent would confound the zealots on both the “short keyword list, no such thing as the long tail” camp and the “the highest value lives in the lowest frequency of clicks” contingency.

Which types of search queries drive more traffic for less spend?

We analyzed about a million dollars worth of spend, spread across roughly 15 million impressions. A few notes on the data:

The advertisers we looked at were a randomly selected mix of lead-gen and e-commerce companies who are small to mid-sized advertisers.

We analyzed the total spend, number of conversions and cost-per conversion against a series of segments.

We divided the data based on the number of clicks per query: 300+, 100-300, 6-100, and 0-5.

All of this activity was on Google’s AdWords.

We were also careful to mix in both long-term and new account data, so that the results wouldn’t be overly skewed by the way our software builds campaigns.

It’s also worth noting that paid search is a multi-billion dollar industry, and this sample size is by no means definitive, but there are nonetheless some very interesting insights to be gained.

First let’s look at the distribution of spend across head, mid, and long tail terms:

The interesting thing about this analysis is that a vast majority of the spend in the accounts in question was directed toward long tail search queries. The aggregate number of search queries in the accounts analyzed was greater than the aggregate number of keywords by about 4 to 1.

This leads to a misconception about the distribution of searcher intent. Many advertisers think that most of the traffic is being driven by a short list of keywords, when in reality these advertisers are spending significantly more of their budgets on the long tail.

Next, let’s look at the distribution of conversions across the same segments:

This data maps pretty consistently with the first data series, showing that these advertisers are driving a majority of their conversions from queries with 0-5 clicks. Additionally, 90 percent of the conversions for this data set were driven by queries with 1-100 clicks attached!

Probably the most interesting data set of all is the cost-per conversion across head, mid and long tail queries:

The cost-per conversion in the 6-99 click segment is less than half of the CPC for more competitive terms, while the particularly low-volume terms (0-5) are roughly two-thirds higher per conversion than the 6-99 click segment.

So what does all this query data mean?

I think the story this data is telling is that:

There is enormous value in the long tail of search queries

Query data has a longer tail than keyword data

To achieve high ROI, it’s crucial to aggressively mine negative keywords and to effectively target more specific search queries.

Here are three key takeaways from our analysis:

There is value in the long tail. The fact that 0-5 click queries and 0-100 click queries comprise so much of the cost and total conversions for these companies is obviously an indication that the long tail of search queries contains a lot of traffic and opportunity.

There’s a longer tail for query data. The fact that the total number of keywords is roughly a fourth of the total number of queries is the result of numerous long tail search queries “hiding,” rolled up under a single keyword. A cursory glance at any of the advertisers accounts might lead one to believe that most of the traffic is being driven by a short list of keywords, when in reality a series of broad matched keywords are matching against more specific, less popular queries.

Negative keywords also have value. The final key takeaway is the product of the discrepancy between the cost-per conversion for 0-5 clicks versus 6-100. While 0-5 clicks drove a majority of the cost and conversions across these advertisers, the 6-100 segment had a significantly lower cost-per conversion.

In theory, the 0-5 segment should be more specific variations of words and phrases. This should mean that by and large they’d be better targeted and more likely to convert. So where’s the gap?

The relatively poorer results in the 0-5 segment are largely because those queries aren’t hyper-targeted: many are the result of broad match aggressively pushing impressions at people that are matched to much broader keywords. Additionally, many irrelevant terms are rolled up in those broad keywords, driving up costs by matching terms people didn’t realize they’d be bidding on to ads for what are now very poorly targeted ads.

Where to go next with search query data and the long tail

While this data certainly isn’t representative of every single advertiser’s experience, it does reveal the need for a strategic approach to combining broad match with negative keyword discovery and implementation, and it emphasizes the importance of discovering, grouping and targeting specific search queries.

Since this article was spurred by a series of recent discussions surrounding the long tail, I thought I’d include links to the articles that inspired this inquiry:

PPC And The Importance of The Long Tail – Additionally, George hosted a great webcast on the subject which went into greater depth on the value of broad match and negative keywords, and offered some advice on how best to implement.

How To Group Your Keyword, Plus a Q and A With WordStream’s Larry Kim – In addition to effectively using negative keywords, you’ll also need to intelligently segment your keywords and find a means for mining query data to assign positive keyword candidates. In this article Josh Dreller interviews the founder of my company, Larry Kim, about how best to attack the issue of grouping and segmentation.

Shifting PPC from Low to High Resolution – Craig Danuloff of Click Equations writes frequently on the subject of gaining deeper insight into your paid search campaigns through closer attention to search query data. This is a nice overview of the Click Equations “high resolution” approach

PPC Man Drowning: Too Many Keywords – Andrew Goodman offers some interesting counter-points to the premise that including large numbers of keywords in a paid search campaign is beneficial

Ding Dong? The Truth About the Life And “Death” of The Long Tail – In this article from May, I walked through some of the benefits of including more granular keyword targets within a paid search campaign, and attempted to draw out the differences between keywords and search queries and the significance of those differences in a bit more detail

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